System Model

A System Model is a representation that is an abstraction framework that models system capabilities, system behaviors, and system relationships for system analysis, system design, and system understanding.

• AKA: Systems Model, System Representation, System Abstraction, System Conceptualization, System Framework.
• Context:
  • It can typically represent System Model Structure through system model component diagrams, system model architectural views, and system model hierarchical organizations.
  • It can typically capture System Model Behavior through system model state diagrams, system model behavioral simulations, and system model dynamic characteristics.
  • It can typically describe System Model Interface via system model interface specifications, system model interaction protocols, and system model communication patterns.
  • It can typically define System Model Process using system model workflow diagrams, system model process specifications, and system model activity sequences.
  • It can typically specify System Model Constraints through system model rules, system model validation criteria, and system model boundary conditions.
  • It can typically establish System Model Abstraction Levels via system model granularity control, system model detail suppression, and system model focus areas.
  • It can typically communicate System Model Purpose through system model objective statements, system model scope definitions, and system model stakeholder perspectives.
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  • It can often enable System Model Analysis with system model verification tools, system model validation techniques, and system model consistency checkers.
  • It can often support System Model Simulation through system model execution engines, system model runtime environments, and system model scenario generators.
  • It can often facilitate System Model Prediction via system model forecasting algorithms, system model scenario analysises, and system model outcome projections.
  • It can often provide System Model Documentation with system model specification languages, system model annotation systems, and system model visualization platforms.
  • It can often incorporate System Model Evolution through system model version control, system model change tracking, and system model refinement processes.
  • It can often validate System Model Correctness using system model formal verification, system model testing frameworks, and system model quality metrics.
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  • It can range from being an Informal System Model to being a Formal System Model, depending on its system model formalization level.
  • It can range from being a Physical System Model to being an Abstract System Model, depending on its system model materiality degree.
  • It can range from being a Simple System Model to being a Complex System Model, depending on its system model complexity level.
  • It can range from being a Static System Model to being a Dynamic System Model, depending on its system model temporal behavior.
  • It can range from being a Deterministic System Model to being a Stochastic System Model, depending on its system model uncertainty handling.
  • It can range from being a Discrete System Model to being a Continuous System Model, depending on its system model state representation.
  • It can range from being a Homogeneous System Model to being a Heterogeneous System Model, depending on its system model component diversity.
  • It can range from being a Monolithic System Model to being a Modular System Model, depending on its system model architectural decomposition.
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  • It can integrate with System Model Development Tools for system model creation support and system model collaborative editing.
  • It can connect to System Model Analysis Systems for system model validation support and system model property verification.
  • It can support System Model Code Generators for system model implementation automation and system model deployment assistance.
  • It can interface with System Model Repositorys for system model version management and system model sharing capabilities.
  • It can leverage System Model Transformation Engines for system model format conversion and system model refinement operations.
  • It can utilize System Model Visualization Tools for system model graphical representation and system model interactive exploration.
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• Examples:
  • Physical System Models, such as:
    • Scale Physical System Models for system model spatial representation, such as:
      • Planetary System Model demonstrating system model structure representation for astronomical system analysis with system model orbital mechanics.
      • Architectural Scale System Model demonstrating system model spatial relationships for building system design with system model structural elements.
      • Molecular System Model demonstrating system model atomic interactions for chemical system simulation with system model bonding patterns.
    • Material Physical System Models for system model substance behavior, such as:
      • Chemical System Model demonstrating system model molecular behavior for chemical system analysis with system model reaction pathways.
      • Mechanical System Model demonstrating system model physical constraints for engineering system design with system model force distributions.
      • Fluid System Model demonstrating system model flow characteristics for hydraulic system optimization with system model pressure gradients.
  • Mathematical System Models, such as:
    • Equation-based System Models for system model analytical representation, such as:
      • Differential Equation System Model demonstrating system model dynamic behavior for continuous system analysis with system model state evolution.
      • Statistical System Model demonstrating system model probabilistic behavior for stochastic system analysis with system model uncertainty quantification.
      • Optimization System Model demonstrating system model objective functions for decision system support with system model constraint satisfaction.
    • Logic-based System Models for system model formal reasoning, such as:
      • Formal Logic System Model demonstrating system model constraint specification for logical system verification with system model proof construction.
      • Petri Net System Model demonstrating system model concurrent behavior for distributed system analysis with system model token flow.
      • Temporal Logic System Model demonstrating system model time-dependent properties for reactive system verification with system model temporal operators.
  • Computational System Models, such as:
    • Software System Models for system model program structure, such as:
      • UML System Model demonstrating system model structural specification for object-oriented system design with system model class relationships.
      • Architecture Description Language System Model demonstrating system model component interfaces for software system architecture with system model connector specifications.
      • Domain-Specific Language System Model demonstrating system model domain abstractions for specialized system development with system model semantic constructs.
    • Simulation System Models for system model executable behavior, such as:
      • Discrete Event System Model demonstrating system model temporal behavior for event-driven system analysis with system model event scheduling.
      • Agent-based System Model demonstrating system model emergent behavior for complex system simulation with system model agent interactions.
      • System Dynamics Model demonstrating system model feedback loops for policy system analysis with system model stock and flow structures.
  • Conceptual System Models, such as:
    • Ontology System Models for system model knowledge representation, such as:
      • Domain Ontology System Model demonstrating system model knowledge structure for semantic system analysis with system model concept hierarchies.
      • Process Ontology System Model demonstrating system model workflow specification for business system modeling with system model activity relationships.
      • Upper Ontology System Model demonstrating system model foundational concepts for cross-domain system integration with system model universal categories.
    • Framework System Models for system model organizational patterns, such as:
      • Enterprise Architecture System Model demonstrating system model organizational structure for business system design with system model capability maps.
      • Reference Architecture System Model demonstrating system model design patterns for standardized system development with system model architectural decisions.
      • Service-Oriented Architecture System Model demonstrating system model service composition for distributed system integration with system model service contracts.
  • Machine Learning System Models, such as:
    • Neural Network System Models demonstrating system model learning architectures for AI system development with system model layer configurations.
    • Probabilistic Graphical System Models demonstrating system model statistical dependencies for inference system design with system model factor graphs.
    • Reinforcement Learning System Models demonstrating system model decision policies for autonomous system behavior with system model reward functions.
  • Business System Models, such as:
    • Business Process System Models demonstrating system model workflow patterns for organizational system analysis with system model role assignments.
    • Value Chain System Models demonstrating system model value creation for strategic system planning with system model activity linkages.
    • Capability System Models demonstrating system model organizational competencies for enterprise system assessment with system model maturity levels.
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• Counter-Examples:
  • System Implementations, which are actual functioning systems rather than system model representations of systems.
  • System Specification Documents, which describe requirements rather than provide system model behavioral simulations.
  • System Blueprints, which show construction details rather than enable system model analysis and system model prediction.
  • System Instances, which are specific system occurrences rather than system model abstractions for analysis.
  • System Prototypes, which are preliminary implementations rather than system model analytical frameworks.
  • System Documentation, which describes system features rather than provides system model executable representations.
• See: System Representation, System Modeling Task, System Analysis, System Simulation, Model-Based Design, System Architecture, Formal Method, System Specification, Model-Driven Engineering, System Thinking, Abstraction, Conceptual Model, Domain Model, Metamodel.

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References

2014

• http://www.w3.org/2013/06/model-ref-20130418.html
  • A "model" refers to an abstract description of the composition and relative dynamic behaviour of the sub-parts of some system

2009

• (Wikipedia, 2009) ⇒ http://en.wikipedia.org/wiki/Model_(physical)
  • A physical model is a smaller or larger physical copy of an object. The object being modelled may be small (for example, an atom) or large (for example, the Solar System).
• (Wikipedia, 2009) ⇒ http://en.wikipedia.org/wiki/Model_(science)
  • Scientific modeling is the process of generating abstract, conceptual, graphical and or mathematical models. Science offers a growing collection of methods, and theory about all kinds of specialized scientific modeling.
• (Wikipedia, 2009) ⇒ http://en.wikipedia.org/wiki/Model_(logic)
  • In mathematics, model theory is the study of (classes of) mathematical structures such as groups, fields, graphs or even models of set theory using tools from mathematical logic. Model theory has close ties to algebra and universal algebra.
• (Wikipedia, 2009) ⇒ http://en.wikipedia.org/wiki/Model_(model_theory)
  • In universal algebra and in model theory, a structure is a type of formal interpretation which consists of an underlying set along with a collection of finitary functions and relations which are defined on it. ...

1998

• (Kohavi & Provost, 1998) ⇒ Ron Kohavi, and Foster Provost. (1998). “Glossary of Terms.” In: Machine Leanring 30(2-3).
  • Model: A structure and corresponding interpretation that summarizes or partially summarizes a set of data, for description or prediction. Most inductive algorithms generate models that can then be used as classifiers, as regressors, as patterns for human consumption, and/or as input to subsequent stages of the KDD process.

1993

• (Gruber, 1993) ⇒ Tom Gruber. (1993). “A translation approach to portable ontology specifications." Knowledge Acquisition, 2(5):199--220.
  • A conceptualization is an abstract, simplified view of the world that we wish to represent for some purpose. Every knowledge base, knowledge-based system, or knowledge-level agent is committed to some conceptualization, explicitly or implicitly. An ontology is an explicit specification of a conceptualization.".

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